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    "\n",
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Interactive Correlation Coefficient With an Outlier\n",
    "\n",
    "\n",
    "### Michael Pyrcz, Professor, The University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
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    "### Correlation Coefficient\n",
    "\n",
    "Here is an interactive workflow demonstrating a limitation of the correlation coefficient, a common metric for analysis the relationship between two feautures, commonly used in inferential and predictive machine learning.\n",
    "\n",
    "I have recorded a walk-through of this interactive dashboard in my [Data Science Interactive Python Demonstrations](https://www.youtube.com/playlist?list=PLG19vXLQHvSDy26fM3hDLg3VCU7U5BGZl) series on my [YouTube](https://www.youtube.com/@GeostatsGuyLectures) channel.\n",
    "\n",
    "* Join me for walk-through of this dashboard [06 Data Science Interactive: Correlation Coefficient](TBD). I'm stoked to guide you and share observations and things to try out!   \n",
    "\n",
    "* I have a lecture on [Correlation Analysis](https://www.youtube.com/watch?v=wZwYEDqB4A4&list=PLG19vXLQHvSB-D4XKYieEku9GQMQyAzjJ&index=21) as part of my [Data Analytics and Geostatistics](https://www.youtube.com/playlist?list=PLG19vXLQHvSB-D4XKYieEku9GQMQyAzjJ) course. Note, for all my recorded lecture the interactive and well-documented workflow demononstrations are available on my GitHub repository [GeostatsGuy's Python Numerical Demos](https://github.com/GeostatsGuy/PythonNumericalDemos).\n",
    "\n",
    "* Also, I have a lecture on [Univariate Statistics](https://www.youtube.com/watch?v=wAcbA2cIqec&list=PLG19vXLQHvSB-D4XKYieEku9GQMQyAzjJ&index=10) as part of my [Data Analytics and Geostatistics](https://www.youtube.com/playlist?list=PLG19vXLQHvSB-D4XKYieEku9GQMQyAzjJ) course.\n",
    "\n",
    "* Also, I have a lecture on [Principal Components Analysis](https://www.youtube.com/watch?v=-to3JXiae9Y&list=PLG19vXLQHvSC2ZKFIkgVpI9fCjkN38kwf&index=17) as part of my [Machine Learning](https://www.youtube.com/playlist?list=PLG19vXLQHvSC2ZKFIkgVpI9fCjkN38kwf) course.\n",
    "\n",
    "#### Bivariate Analysis\n",
    "\n",
    "Quantify the relationship between two features:\n",
    "\n",
    "* e.g., relationship between porosity and permeability, nickle and copper grade, etc.\n",
    "\n",
    "What would be the impact if we ignore this relationship and simply modeled eacf of our features independently?\n",
    "\n",
    "* no relationship beyond constraints at data locations\n",
    "* independent away from data\n",
    "* nonphysical results, unrealistic uncertainty models\n",
    "\n",
    "We must quantify relationships between our features and integrate them in our models.\n",
    "\n",
    "#### Correlation Coefficient\n",
    "\n",
    "Pearson’s Product‐Moment Correlation Coefficient\n",
    "* provides a measure of the degree of linear relationship.\n",
    "* we refer to it as the 'correlation coefficient'\n",
    "\n",
    "Let's review the sample variance of feature, $\\sigma^2_x$, where $X$ feature is represented by a set of samples a locations in our modeling space, $x(\\bf{u}_\\alpha)$,  $\\alpha = 1, \\dots, n$, but we will use the shorthand,  $x_{i}$,  $i = 1, \\dots, n$.\n",
    "\n",
    "\\begin{equation}\n",
    "\\sigma^2_{x}  = \\frac{\\sum_{i=1}^{n} (x_i - \\overline{x})^2}{(n-1)}\n",
    "\\end{equation}\n",
    "\n",
    "We can expand the the squared term and replace on of them with $Y$, another feature in addition to $X$.\n",
    "\n",
    "\\begin{equation}\n",
    "C_{x,y}  = \\frac{\\sum_{i=1}^{n} (x_i - \\overline{x})(y_i - \\overline{y})}{(n-1)}\n",
    "\\end{equation}\n",
    "\n",
    "We now have a measure that represents the manner in which features $X$ and $Y$ co-vary or vary together.  We can standardized the covariance by the product of the standard deviations of $X$ and $Y$ to calculate the correlation coefficent. \n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_{x,y}  = \\frac{\\sum_{i=1}^{n} (x_i - \\overline{x})(y_i - \\overline{y})}{(n-1)\\sigma_x \\sigma_y}, \\, -1.0 \\le \\rho_{x,y} \\le 1.0\n",
    "\\end{equation}\n",
    "\n",
    "#### Interpreting the Correlation Coefficient\n",
    "\n",
    "Given a bivariate Gaussian distribution, we can make the following interpretations of correlation coefficients:\n",
    "\n",
    "* $\\rho_{x,y} = 1.0$ - perfect positive linear relationship\n",
    "* $\\rho_{x,y} = 0.0$ - no relationship\n",
    "* $\\rho_{x,y} = -1.0$ - perfect negative linear relationship\n",
    "\n",
    "if not bivariate Gaussian this does not hold.\n",
    "\n",
    "In summary we can state that the correlation coefficient is related to the covariance as:\n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_{x,y}  = \\frac{C_{x,y}}{\\sigma_x \\sigma_y}\n",
    "\\end{equation}\n",
    "\n",
    "The correlation coefficient provides a useful metrics to quantify relationships between two features at a time. We can also consider bivariate scatter plots and matrix scatter plots to visualize multivariate data.\n",
    "\n",
    "Other applications in machine learning with correlation coefficients, e.g.:\n",
    "\n",
    "- the variance explained metric for linear regression is the correlation coefficient squared\n",
    "\n",
    "\\begin{equation}\n",
    "R^2_{X,Y} = \\left( \\rho_{x,y} \\right)^2 \n",
    "\\end{equation}\n",
    "\n",
    "- principal components analysis are calculated as the Eigen values and vectors from the features' covariance matrix, the unstandardized correlation coefficient.\n",
    "\n",
    "#### Limitations of the Correlation Coefficient\n",
    "\n",
    "These are important limitations of the Pearson's product moment correlation coefficient:\n",
    "\n",
    "1. measures the strength of the linear relationship between the two features. \n",
    "2. **sensitive to outliers**\n",
    "3. assumes finite covariance, $C_{X,Y}$, and finite variances, $\\sigma^2_X$, and $\\sigma^2_Y$. \n",
    "4. only when the features are bivariate normal does the correlation coefficient provide a complete, unique description of the relationship between the two features, this includes homoscedasticity (constant conditional variance).\n",
    "\n",
    "#### Spearman Rank Correlation Coefficient\n",
    "\n",
    "The Person's correlation coefficient is quite sensitive to outliers and depature from linear behavoir (in the bivariate sense).  We have an altenrative known as the Spearman's rank correlations coefficient. \n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_{R_x,R_y}  = \\frac{\\sum_{i=1}^{n} (R_{x_i} - \\overline{R_x})(R_{y_i} - \\overline{R_y})} {(n-1)\\sigma_{R_x}\\sigma_{R_y}},  \\ \\ -1.0 \\le \\rho_{R_x,R_y} \\le 1.0\n",
    "\\end{equation}\n",
    "\n",
    "The rank correlation applies the rank transform to the data prior to calculating the correlation coefficent. To calculate the rank transform simply replace the data values with the rank $R_x = 1, \\dots,n$, where $n$ is the maximum value and $1$ is the minimum value.\n",
    "\n",
    "Given the samples are sorted in ascending order:\n",
    "\n",
    "\\begin{equation}\n",
    "R_{x_i} = i, \\ \\  i = 1,\\dots, n \n",
    "\\end{equation}\n",
    "\n",
    "#### Advantages of the Spearman Rank over the Pearson Product Moment Correlation Coefficient\n",
    "\n",
    "In general, the Spearman rank correlation coefficient is more robust in the presence of:\n",
    "\n",
    "* outliers \n",
    "* monotonic nonlinearity\n",
    "\n",
    "The corelation coefficients provide useful metrics to quantify relationships between two variables at a time. We can also consider bivariate scatter plots and matrix scatter plots to visualize multivariate data. In general, current practical \n",
    "subsurface modeling is bivariate, two variables at a time.\n",
    "\n",
    "#### Demonstration of a Rank Transform\n",
    "\n",
    "What does the rank correlation coefficient see? Here's a dataset with an outlier before and after rank transform."
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import rankdata\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "sample = np.random.multivariate_normal([0.0,0.0],[[1.0,0.8],[0.8,1.0]],size = 500)\n",
    "sample[-1,0] = 5.5; sample[-1,1] = 5.5 \n",
    "sample_rank = rankdata(sample,axis=0)\n",
    "plt.subplot(121)\n",
    "plt.scatter(sample[:,0],sample[:,1],c='darkorange',alpha=0.8,edgecolor='black'); plt.xlim([-3,6]); plt.ylim([-3,6]); plt.xlabel(r'$X_1$'); plt.ylabel(r'$X_2$'); plt.title('Original Features')\n",
    "plt.scatter(sample[-1,0],sample[-1,1],c='red',alpha=1.0,s=80,edgecolor='black',zorder=100);\n",
    "plt.subplot(122)\n",
    "plt.scatter(sample_rank[:,0],sample_rank[:,1],c='darkorange',alpha=0.8,edgecolor='black'); plt.xlim([0,520]); plt.ylim([0,520]); plt.xlabel(r'$R_{X_1}$'); plt.ylabel(r'$R_{X_2}$'); plt.title('Rank Transformed Features')\n",
    "plt.scatter(sample_rank[-1,0],sample_rank[-1,1],c='red',alpha=1.0,s=80,edgecolor='black',zorder=100);\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.3); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation Coefficient Dashboard\n",
    "\n",
    "To demonstrate the correlation coefficient with an outlier, I built out 1 dashboard:\n",
    "\n",
    "* change the correlation coefficient and move around a single outlier and observe the impact on the Pearson and Spearman correlation coefficients.\n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "\n",
    "That's all!\n",
    "\n",
    "#### Load the Required Libraries\n",
    "\n",
    "We will also need some standard Python packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os                                               # to set current working directory \n",
    "import sys                                              # supress output to screen for interactive variogram modeling\n",
    "import io\n",
    "import numpy as np                                      # arrays and matrix math\n",
    "import pandas as pd                                     # DataFrames\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt                         # plotting\n",
    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator) # control of axes ticks\n",
    "from matplotlib.pyplot import cm                        # color maps\n",
    "from matplotlib.patches import Ellipse                  # plot an ellipse\n",
    "import math                                             # sqrt operator\n",
    "import random                                           # random simulation locations\n",
    "from copy import copy                                   # copy a colormap\n",
    "from scipy.stats import norm\n",
    "from ipywidgets import interactive                      # widgets and interactivity\n",
    "from ipywidgets import widgets                            \n",
    "from ipywidgets import Layout\n",
    "from ipywidgets import Label\n",
    "from ipywidgets import VBox, HBox\n",
    "from scipy.stats import norm                            # Gaussian distribution\n",
    "import scipy.stats as st                                # statistical methods\n",
    "plt.rc('axes', axisbelow=True)                          # set axes and grids in the background for all plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  \n",
    "\n",
    "#### Declare Functions\n",
    "\n",
    "I just added a convenience function for adding major and minor gridlines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_grid(sub_plot):\n",
    "    sub_plot.grid(True, which='major',linewidth = 1.0); sub_plot.grid(True, which='minor',linewidth = 0.2) # add y grids\n",
    "    sub_plot.tick_params(which='major',length=7); sub_plot.tick_params(which='minor', length=4)\n",
    "    sub_plot.xaxis.set_minor_locator(AutoMinorLocator()); sub_plot.yaxis.set_minor_locator(AutoMinorLocator()) # turn on minor ticks   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interactive Correlation Coefficient with an Outlier\n",
    "\n",
    "Now let's set up our dashboard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings; warnings.simplefilter('ignore')\n",
    "\n",
    "# dashboard: number of simulation locations and variogram parameters\n",
    "style = {'description_width': 'initial'}\n",
    "l = widgets.Text(value='                                                  Correlation Coefficient with an Outlier, Michael Pyrcz, Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "ndata = widgets.IntSlider(min = 5, max = 1000, value = 500, step = 100, description = r'$n_{samples}$',orientation='horizontal',continuous_update=False,\n",
    "                          layout=Layout(width='600px', height='40px'))\n",
    "ndata.style.handle_color = 'gray'\n",
    "\n",
    "corr = widgets.FloatSlider(min = -1.0, max = 1.0, value = 0, step = 0.1, description = r'$\\rho_{x_1,x_2}$',orientation='horizontal',continuous_update=False,\n",
    "                          layout=Layout(width='600px', height='40px'))\n",
    "\n",
    "ox = widgets.FloatSlider(min = 1.0, max = 2.8, value = 1, step = 0.1, description = r'$log(x_{n+1})$',orientation='horizontal',continuous_update=False,\n",
    "                          layout=Layout(width='600px', height='40px'))\n",
    "\n",
    "oy = widgets.FloatSlider(min = 1.0, max = 2.8, value = 1, step = 0.1, description = r'$log(y_{n+1})$',orientation='horizontal',continuous_update=False,\n",
    "                          layout=Layout(width='600px', height='40px'))\n",
    "\n",
    "corr.style.handle_color = 'gray'\n",
    "\n",
    "uipars = widgets.HBox([ndata,corr,ox,oy],)     \n",
    "\n",
    "uik = widgets.VBox([l,uipars],)\n",
    "\n",
    "def f_make(ndata,corr,ox,oy): # function to take parameters, make sample and plot\n",
    "    nonlin_wt = 1.0\n",
    "    ox = 10**ox; oy = 10**oy\n",
    "    text_trap = io.StringIO()                           # suppress all text function output to dashboard to avoid clutter \n",
    "    sys.stdout = text_trap\n",
    "    cmap = cm.inferno\n",
    "    np.random.seed(seed = 73072)                        # ensure same results for all runs\n",
    "    mean = np.array([10,10])\n",
    "    correl = np.array([[2.0,corr*2.0],[corr*2.0,2.0]],dtype=float)\n",
    "    sample = np.random.multivariate_normal(mean,correl,size = ndata)\n",
    "    sample = np.vstack([sample,[ox,oy]])\n",
    "    slope, intercept, r_value, p_value, std_err = st.linregress(sample[:,0],sample[:,1])\n",
    "    xmin = min(-3,ox-1); xmax = max(3,ox+1); ymin = min(-3,oy-1); ymax = max(3,oy+1)\n",
    "    xmin = 1; ymin = 1; xmax = 1000; ymax = 1000\n",
    "    x1 = np.array([xmin,xmax])\n",
    "    x2 = x1*slope + intercept\n",
    "    \n",
    "    nbin = int(ndata / 10)\n",
    "    plt_scatter = plt.subplot2grid((3, 3), (1, 0), rowspan=2, colspan=2)\n",
    "    plt_x1 = plt.subplot2grid((3, 3), (0, 0), colspan=2,\n",
    "                               sharex=plt_scatter)\n",
    "    plt_x2 = plt.subplot2grid((3, 3), (1, 2), rowspan=2,\n",
    "                               sharey=plt_scatter)    \n",
    "    \n",
    "    #plt.plot([0,0],[1.0,1.0],color = 'black')\n",
    "    #plt_scatter.plot(x1,x2,color = 'black',label = r'$X_2 = f(X_1)$')\n",
    "    plt_scatter.scatter(sample[:ndata-1,0],sample[:ndata-1,1],color = 'red',alpha = 0.05,edgecolors='black',label = 'Samples')\n",
    "    #plt_scatter.scatter(ox,oy,color = 'blue',alpha = 1.0,marker='s',edgecolors='black',label = 'Outlier')\n",
    "    plt_scatter.scatter(ox,oy,s=40,marker='x',color = 'black',alpha = 1.0,zorder=100,label='Outlier')\n",
    "    plt_scatter.scatter(ox,oy,s=200,marker='o',lw=1.0,edgecolor = 'black',facecolor = 'white',alpha = 1.0,zorder=98)\n",
    "    \n",
    "    import matplotlib.ticker as ticker \n",
    "    \n",
    "    plt_scatter.set_xlabel(r'$x_1$')\n",
    "    plt_scatter.set_ylabel(r'$x_2$')\n",
    "    plt_scatter.set_xlim([xmin,xmax])\n",
    "    plt_scatter.set_ylim([ymin,ymax])\n",
    "    plt_scatter.legend(loc='upper left') \n",
    "    plt_scatter.set_xscale('log'); plt_scatter.set_yscale('log')\n",
    "    \n",
    "    corr = stats.pearsonr(sample[:,0],sample[:,1])[0]\n",
    "    plt_scatter.annotate(r'Pearson: $\\rho_{x_1,x_2}$ = ' + str(np.round(corr,3)),(xmin+(xmax-xmin)*0.02,ymax-(ymax-ymin)*0.9985),size=15)\n",
    "    corrs = stats.spearmanr(sample[:,0],sample[:,1])[0]    \n",
    "    plt_scatter.annotate(r'Spearman: $\\rho_{R_{x_1},R_{x_2}}$ = ' + str(np.round(corrs,3)),(xmin+(xmax-xmin)*0.02,ymax-(ymax-ymin)*0.9995),size=15)\n",
    "    \n",
    "    plt_x1.hist(sample[:,0],density = True,color='red',alpha=0.8,edgecolor='black',bins=np.logspace(np.log10(xmin),np.log10(xmax),nbin))\n",
    "    plt_x1.set_ylim([0.0,0.3]); add_grid(plt_x1)\n",
    "    plt_x1.set_xlabel(r'$x_1$'); plt_x1.set_ylabel(r'Density')\n",
    "    plt_x1.set_title(r'Bivariate Gaussian Distributed Data with $\\rho =$' + str(round(corr,2)) + ' & 1 Outlier')\n",
    "    \n",
    "    plt_x2.hist(sample[:,1],orientation='horizontal',density = True,color='red',alpha=0.8,edgecolor='black',bins=np.logspace(np.log10(ymin),np.log10(ymax),nbin))\n",
    "    plt_x2.set_xlim([0.0,0.3]); plt_x2.set_ylabel(r'$x_2$'); plt_x2.set_xlabel(r'Density'); add_grid(plt_x2)\n",
    "    plt_scatter.set_ylabel(r'$x_2$'); \n",
    "    \n",
    "    \n",
    "    locmin = ticker.LogLocator(base=10.0,subs=np.arange(0.1,0.9,0.1),numticks=12)\n",
    "    plt_scatter.yaxis.set_minor_locator(locmin); plt_scatter.xaxis.set_minor_locator(locmin)\n",
    "    plt_scatter.grid(which = 'both',axis = 'both', linewidth=0.3)\n",
    "    plt_scatter.grid(which = 'major',axis = 'both', linewidth=1)\n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=1.3, top=1.6, wspace=0.3, hspace=0.3); plt.show()\n",
    "    \n",
    "# connect the function to make the samples and plot to the widgets    \n",
    "interactive_plot = widgets.interactive_output(f_make, {'ndata':ndata,'corr':corr,'ox':ox,'oy':oy})\n",
    "#interactive_plot.clear_output(wait = True)               # reduce flickering by delaying plot updating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Correlation Coefficient Demonstration\n",
    "\n",
    "* select the number of data, correlation coefficient, an outlier and compare the Pearson product-momment and Spearman's rank correlation coefficients. \n",
    "\n",
    "#### Michael Pyrcz, Professor, The University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)\n",
    "\n",
    "### The Inputs\n",
    "\n",
    "Select the number of samples and the Pearson product-moment correlation coefficient:\n",
    "\n",
    "* **$n_{samples}$**: number of samples, **$\\rho_{x_1,x_2}$**: the Pearson product-moment correlation\n",
    "* **$log(x_{n+1}$**, **$log(y_{n+1}$**: location of a single outlier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
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    {
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       "model_id": "b4f1c83ee10e46fcaefcb5621ba64a54",
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      "text/plain": [
       "VBox(children=(Text(value='                                                  Correlation Coefficient with an O…"
      ]
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       "Output()"
      ]
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   ],
   "source": [
    "display(uik, interactive_plot)                            # display the interactive plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This was an interactive demonstration of the impact of an outlier on the Pearson product moment and Spearman's rank correlation coefficients. I am providing students an opportunity to play with data analytics, geostatistics and machine learning for experiential learning.\n",
    "  \n",
    "#### The Author:\n",
    "\n",
    "### Michael Pyrcz, Professor, The University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Professor, Cockrell School of Engineering and The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)  \n",
    "  "
   ]
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